155 citations · 955 across the 56 of their papers we have counts for
10 papers · 1 filter
No Free Lunch for Stochastic Gradient Langevin Dynamics
Natesh S. Pillai, Aaron Smith, Azeem Zaman
As sample sizes grow, scalability has become a central concern in the development of Markov chain Monte Carlo (MCMC) methods. One general approach to this problem, exemplified by t…
Policy Gradients for Optimal Parallel Tempering MCMC
Daniel Zhao, Natesh S. Pillai
Parallel tempering is a meta-algorithm for Markov Chain Monte Carlo that uses multiple chains to sample from tempered versions of the target distribution, enhancing mixing in multi…
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions
Natesh S. Pillai
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm that incorporates the gradient of the logarithm of the target density in its proposal distribution. In an…
No Free Lunch for Approximate MCMC
James E. Johndrow, Natesh S. Pillai, Aaron Smith
It is widely known that the performance of Markov chain Monte Carlo (MCMC) can degrade quickly when targeting computationally expensive posterior distributions, such as when the sa…
Rate-optimal refinement strategies for local approximation MCMC
Andrew D. Davis, Youssef Marzouk, Aaron Smith +1
Many Bayesian inference problems involve target distributions whose density functions are computationally expensive to evaluate. Replacing the target density with a local approxima…
Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation
Maxime Rischard, Pierre E. Jacob, Natesh Pillai
Posterior distributions often feature intractable normalizing constants, called marginal likelihoods or evidence, that are useful for model comparison via Bayes factors. This has m…
Parallel local approximation MCMC for expensive models
Patrick Conrad, Andrew Davis, Youssef Marzouk +2
Performing Bayesian inference via Markov chain Monte Carlo (MCMC) can be exceedingly expensive when posterior evaluations invoke the evaluation of a computationally expensive model…
Asymptotic Analysis of the Random-Walk Metropolis Algorithm on Ridged Densities
Alexandros Beskos, Gareth Roberts, Alexandre Thiery +1
In this paper we study the asymptotic behavior of the Random-Walk Metropolis algorithm on probability densities with two different `scales', where most of the probability mass is d…
The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Luke Bornn, Natesh Pillai, Aaron Smith +1
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For…
Why approximate Bayesian computational (ABC) methods cannot handle model choice problems
Christian Robert, Jean-Michel Marin, Natesh S. Pillai
Approximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population g…